Traffic cameras have always been useful for collecting data—as long as you don’t mind sifting through countless of hours of raw traffic footage by hand. But in Texas, with the help of the Stampede 2 supercomputer—the 12th fastest computer in the world—researchers have developed an AI algorithm that removes a lot of the legwork from collecting and analyzing traffic data.

The AI algorithm has trained a supercomputer to identify, label and track objects present in traffic camera footage. Once the computer can see and name different objects—like cars and pedestrians—it can analyze relationships and trends between them, generating useful information about the way they interact over time and in space. The system also generates a searchable traffic database, negating the need for scientists to comb through reels of footage to make meaningful observations.

The system has wide-ranging implications for assisting researchers and planners in more quickly and accurately understand what’s going on in the transportation system, including finding problem spots where conflicts frequently occur. In initial tests, for example, developers used the system to count vehicles on the road with 95% accuracy. They also used it to identify close encounters and unsafe interactions between vehicles and pedestrians.

The developers say they will continue to explore different ways the new algorithm can be applied to enhance traffic management and safety—such as pinpointing locations where pedestrians frequently break traffic laws, or understanding the way drivers react to different forms of signage. Plans to use the technology to enhance signal timing throughout the city of Austin are already taking shape.

Ultimately, the new method of data collection and analysis demonstrates the profound impact that computers and artificial intelligence will have on the transportation system in the coming years and decades. Prepare to embark on a new era.